We study a modular approach to tackle long-horizon mobile manipulation tasks for object rearrangement, which decomposes a full task into a sequence of subtasks. To tackle the entire task, prior work chains multiple stationary manipulation skills with a point-goal navigation skill, which are learned individually on subtasks. Although more effective than monolithic end-to-end RL policies, this framework suffers from compounding errors in skill chaining, e.g., navigating to a bad location where a stationary manipulation skill can not reach its target to manipulate. To this end, we propose that the manipulation skills should include mobility to have flexibility in interacting with the target object from multiple locations and at the same time the navigation skill could have multiple end points which lead to successful manipulation. We operationalize these ideas by implementing mobile manipulation skills rather than stationary ones and training a navigation skill trained with region goal instead of point goal. We evaluate our multi-skill mobile manipulation method M3 on 3 challenging long-horizon mobile manipulation tasks in the Home Assistant Benchmark (HAB), and show superior performance as compared to the baselines.

Latency-critical services have been widely deployed in cloud environments. For cost-efficiency, multiple services are usually co-located on a server. Thus, run-time resource scheduling becomes the pivot for QoS control in these complicated co-location cases. However, the scheduling exploration space enlarges rapidly with the increasing server resources, making the schedulers hardly provide ideal solutions quickly. More importantly, we observe that there are "resource cliffs" in the scheduling exploration space. They affect the exploration efficiency and always lead to severe QoS fluctuations. Resource cliffs cannot be easily avoided in previous schedulers. To address these problems, we propose a novel ML-based intelligent scheduler - OSML. It learns the correlation between architectural hints (e.g., IPC, cache misses, memory footprint, etc.), scheduling solutions and the QoS demands based on a data set we collected from 11 widely deployed services running on off-the-shelf servers. OSML employs multiple ML models to work collaboratively to predict QoS variations, shepherd the scheduling, and recover from QoS violations in complicated co-location cases. OSML can intelligently avoid resource cliffs during scheduling and reach an optimal solution much faster than previous approaches for co-located LC services. Experimental results show that OSML supports higher loads and meets QoS targets with lower scheduling overheads and shorter convergence time than previous studies.

We study the problem of target stabilization with robust obstacle avoidance in robots and vehicles that have access only to vision-based sensors for the purpose of realtime localization. This problem is particularly challenging due to the topological obstructions induced by the obstacle, which preclude the existence of smooth feedback controllers able to achieve simultaneous stabilization and robust obstacle avoidance. To overcome this issue, we develop a vision-based hybrid controller that switches between two different feedback laws depending on the current position of the vehicle using a hysteresis mechanism and a data-assisted supervisor. The main innovation of the paper is the incorporation of suitable perception maps into the hybrid controller. These maps can be learned from data obtained from cameras in the vehicles and trained via convolutional neural networks (CNN). Under suitable assumptions on this perception map, we establish theoretical guarantees for the trajectories of the vehicle in terms of convergence and obstacle avoidance. Moreover, the proposed vision-based hybrid controller is numerically tested under different scenarios, including noisy data, sensors with failures, and cameras with occlusions.

In this paper, a novel knowledge-based genetic algorithm for path planning of a mobile robot in unstructured complex environments is proposed, where five problem-specific operators are developed for efficient robot path planning. The proposed genetic algorithm incorporates the domain knowledge of robot path planning into its specialized operators, some of which also combine a local search technique. A unique and simple representation of the robot path is proposed and a simple but effective path evaluation method is developed, where the collisions can be accurately detected and the quality of a robot path is well reflected. The proposed algorithm is capable of finding a near-optimal robot path in both static and dynamic complex environments. The effectiveness and efficiency of the proposed algorithm are demonstrated by simulation studies. The irreplaceable role of the specialized genetic operators in the proposed genetic algorithm for solving the robot path planning problem is demonstrated through a comparison study.

This paper considers the simultaneous position and orientation planning of nonholonomic multirobot systems. Different from common researches which only focus on final position constraints, we model the nonholonomic mobile robot as a rigid body and introduce the orientation as well as position constraints for the robot's final states. In other words, robots should not only reach the specified positions, but also point to the desired orientations simultaneously. The challenge of this problem lies in the underactuation of full-state motion planning, since three states need to be planned by mere two control inputs. To this end, we propose a dynamic vector field (DVF) based on the rigid body modeling. Specifically, the dynamics of the robot orientation are brought into the vector field, implying that the vector field is not static on the 2-D plane anymore, but a dynamic one varying with the attitude angle. Hence, each robot can move along the integral curve of the DVF to arrive at the desired position, and in the meantime, the attitude angle can converge to the specified value following the orientation dynamics. Subsequently, by designing a circular vector field under the framework of the DVF, we further study the obstacle avoidance and mutual-robot-collision avoidance in the motion planning. Finally, numerical simulation examples are provided to verify the effectiveness of the proposed methodology.

This article introduces a multimodal motion planning (MMP) algorithm that combines three-dimensional (3-D) path planning and a DWA obstacle avoidance algorithm. The algorithms aim to plan the path and motion of obstacle-overcoming robots in complex unstructured scenes. A novel A-star algorithm is proposed to combine the characteristics of unstructured scenes and a strategy to switch it into a greedy best-first strategy algorithm. Meanwhile, the algorithm of path planning is integrated with the DWA algorithm so that the robot can perform local dynamic obstacle avoidance during the movement along the global planned path. Furthermore, when the proposed global path planning algorithm combines with the local obstacle avoidance algorithm, the robot can correct the path after obstacle avoidance and obstacle overcoming. The simulation experiments in a factory with several complex environments verified the feasibility and robustness of the algorithms. The algorithms can quickly generate a reasonable 3-D path for obstacle-overcoming robots and perform reliable local obstacle avoidance under the premise of considering the characteristics of the scene and motion obstacles.

Intention deception involves computing a strategy which deceives the opponent into a wrong belief about the agent's intention or objective. This paper studies a class of probabilistic planning problems with intention deception and investigates how a defender's limited sensing modality can be exploited by an attacker to achieve its attack objective almost surely (with probability one) while hiding its intention. In particular, we model the attack planning in a stochastic system modeled as a Markov decision process (MDP). The attacker is to reach some target states while avoiding unsafe states in the system and knows that his behavior is monitored by a defender with partial observations. Given partial state observations for the defender, we develop qualitative intention deception planning algorithms that construct attack strategies to play against an action-visible defender and an action-invisible defender, respectively. The synthesized attack strategy not only ensures the attack objective is satisfied almost surely but also deceives the defender into believing that the observed behavior is generated by a normal/legitimate user and thus failing to detect the presence of an attack. We show the proposed algorithms are correct and complete and illustrate the deceptive planning methods with examples.

This paper proposes a novel inverse kinematics (IK) solver of articulated robotic systems for path planning. IK is a traditional but essential problem for robot manipulation. Recently, data-driven methods have been proposed to quickly solve the IK for path planning. These methods can handle a large amount of IK requests at once with the advantage of GPUs. However, the accuracy is still low, and the model requires considerable time for training. Therefore, we propose an IK solver that improves accuracy and memory efficiency by utilizing the continuous hidden dynamics of Neural ODE. The performance is compared using multiple robots.

Suppose we have a robot in a simple world like the one below. Let's consider commanding our robot to perform a task such as "take the apple from the shelf and put it on the table". Simple task planning example world: A robot can move between a finite set of locations, and can pick and place objects at those locations. I would argue we humans have pretty good intuition for how a robot could achieve this task. We could describe what the robot should do by breaking the solution down into individual actions.

Starting with our very first store on Ocean Avenue in San Francisco, opened almost 50 years ago by Doris and Don Fisher. The thread that's run through those five decades is the phenomenal people that make up our brand – our employees and our customers. People who are rooted in the Legacy that makes Gap what it is, but who are also focused on the future. People who want to leave the world better than they found it. We've built our brand on staying true to our roots while always being out in front of what's next. If you want to be part of an iconic American brand, and help lead the way for where we're headed, we'd love to have you join us About the Role* In this role, you will support the store leadership team by performing functional tasks as assigned. You will act as a mentor and role model to employees to support service behaviors and the execution of tasks in specific areas of expertise. You will focus on leading processes and/or areas of the business, executing tasks and maintaining productivity to ensure goals are met. Through collaboration with your leadership team, your goal is to teach and coach your team and drive behaviors to deliver a best-in-class customer experience What You'll Do* Serve as a role model to achieve priorities in store, with the customer as the primary focus Support the store leadership team to collaborate effectively with employees and ensure work tasks are completed in a timely and efficient manner Build and share expertise in an assigned specialized functional area Support completion or work processes before or after the store closes as needed inclusive of opening and/or closing the store Listen and ask questions to solicit feedback to understand needs and provide service Handle unique or complex customer interactions.Who You Are* Provides clear and direct communication of expectations and gives feedback Ability to utilize technology effectively and engage with customers and your team to meet goals Able to effectively lead and inspire others through coaching and mentoring Demonstrate interest and initiative towards continuous improvement and growth Research process or transaction flow to identify root cause of errors. One of the most competitive Paid Time Off plans in the industry.* Employees can take up to five "on the clock" hours each month to volunteer at a charity of their choice.